Economic Feasibility of Transporting Natural Gas Hydrates in Slurry Pipelines
Bibliographic record
Abstract
Natural Gas Hydrates are cage-like structures that are composed of natural gas (methane, ethane, etc.) molecules contained or entrapped within a water lattice. The hydrate structure contains tightly packed gas in ratios of over 160 to 1. Thus, there is a huge conceived upside to transporting the gas in this mode efficiency-wise if one could transport hydrates to a central processing facility where the hydrate would be processed to meet natural gas pipeline grid specifications. The question is: can they be transported in slurry form with water or oil as a carrier fluid, and what are the pros and cons of such mode of transportation. This paper attempts to answer these questions, and presents a feasibility analysis of three pipeline transportation scenarios to transport equivalent of 116 MMSCFD of natural gas over 500 km distance. It was found that for transportation of natural gas in the form of hydrates to be economically feasible, it has to be combined with transportation of crude oil as a carrying fluid rather than water, so that the cost of transportation per unit energy of the combined hydrates/oil slurry mixture is shared between the two energy commodities. This will result in even a lower cost below that of conventional transportation of natural gas in gaseous (vapour) form.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".